Data sources for scoring batch deployments

Inline data description

Inline type input data for batch processing is specified in the batch deployment job's payload. For example, you can pass a CSV file as the deployment input in the UI or as a value for the scoring.input_data parameter in a notebook. When the batch deployment job is completed, the output is written to the corresponding job's scoring.predictions metadata parameter.

Data reference description

Input and output data of type data reference that is used for batch processing can be stored:

• In a remote data source, for example, a cloud storage bucket or an SQL or no-SQL database.
• As a local or managed data asset in a deployment space.

Details for data references include:

• Data source reference type depends on the asset type. Refer to Data source reference types section in Adding data assets to a deployment space.

• For data_asset type, the reference to input data must be specified as a /v2/assets href in the input_data_references.location.href parameter in the deployment job's payload. The data asset that is specified is a reference to a local or a connected data asset. Before release 5.2.1: if the batch deployment job's output data must be persisted in a remote data source, the references to output data must be specified as a /v2/assets href in output_data_reference.location.href parameter in the deployment job's payload. 5.2.1 and later From release 5.2.1: replacing or appending to an existing local data asset is not possible. This means that you can't use output_data_reference.location.href as the output location. Instead, you must use output_data_reference.location.name.

• Any input and output data_asset references must be in the same space ID as the batch deployment.

• If the batch deployment job's output data must be persisted in a deployment space as a local asset, output_data_reference.location.name must be specified. When the batch deployment job is completed successfully, the asset with the specified name is created in the space.

• Output data can contain information on where in a remote database the data asset is located. In this situation, you can specify whether to append the batch output to the table or truncate the table and update the output data. Use the output_data_references.location.write_mode parameter to specify the values truncate or append.

  • Specifying truncate as value truncates the table and inserts the batch output data.
  • Specifying append as value appends the batch output data to the remote database table.
  • write_mode is applicable only for the output_data_references parameter.
  • write_mode is applicable only for data assets in remote databases. This parameter is not applicable for a local data assets or assets located in a local cloud storage bucket.

"input_data_references": [{
    "type": "data_asset",
    "connection": {
    },
    "location": {
        "href": "/v2/assets/<asset_id>?space_id=<space_id>"
    }
}]

"input_data_references": [{
    "type": "connection_asset",
    "connection": {
        "id": "<connection_guid>"
    },
    "location": {
        "bucket": "<bucket name>",
        "file_name": "<directory_name>/<file name>"
    }
    <other wdp-properties supported by runtimes>
}]

Preparing a payload that matches the schema of an existing model

Refer to this sample code:

model_details = client.repository.get_details("<model_id>")  # retrieves details and includes schema
columns_in_schema = []
for i in range(0, len(model_details['entity']['schemas']['input'][0].get('fields'))):
    columns_in_schema.append(model_details['entity']['schemas']['input'][0].get('fields')[i]['name'])

X = X[columns_in_schema] # where X is a pandas dataframe that contains values to be scored
#(...)
scoring_values = X.values.tolist()
array_of_input_fields = X.columns.tolist()
payload_scoring = {"input_data": [{"fields": [array_of_input_fields],"values": scoring_values}]}